The role of Chief Operating Officers is undergoing a fundamental transformation. Artificial intelligence is reshaping how operations leaders create efficiencies, build resilience, and foster collaboration, moving organizations from reactive problem-solving to predictive orchestration. This shift is particularly pronounced in technology companies, where COOs now manage a “Digital Supply Chain” of code, data, and customer value rather than physical inventory.
AI in operations management refers to the use of artificial intelligence to optimize workflows, predict operational risks, automate decision-making, and continuously improve performance across engineering, finance, customer operations, and supply chains.
The Evolving Mandate: From Efficiency to Velocity
Operations management has traditionally focused on EBITDA optimization and cost control. Today’s COOs face a different mandate: velocity. How quickly can an idea become a shipped feature? How fast can a lead convert to a renewed customer? These questions define operational excellence in the AI era.
The convergence of COO, CIO, and CTO roles reflects this shift. Modern operations leaders must balance immediate operational needs with long-term AI-enabled transformation, what industry experts call “running at two speeds at once.” This requires transitioning from being the best operators to becoming the best end-to-end thinkers who can integrate technology, processes, and people into cohesive systems.
Scaling AI in Operations Management Beyond Pilots
One of the most pressing challenges facing operations leaders is escaping “pilot purgatory.” Research from MIT indicates that 95 percent of tech-driven pilots get stuck without ever reaching scale. The pattern is familiar: organizations design a pilot for one department, prove it works, but discover that scaling requires completely different activities, skills, capabilities, and technology investments.
The solution lies in thinking at scale from day one. Successful organizations focus on business areas critical to their performance goals and design for enterprise-wide deployment from the initial planning stage. Rather than celebrating pilot success, leading COOs ask: “What infrastructure, governance, and change management will we need to deploy this across 50 locations or 5,000 employees?”
Key Use Cases of AI in Operations Management
The table below shows how AI in operations management changes core operational functions compared to traditional approaches. It highlights where predictive analytics, automation, and real-time decision-making create the biggest operational impact.
1. Engineering Operations: The Digital Factory Floor
For technology companies, the engineering department is the factory floor. AI is radically altering production metrics here, but not always in expected ways.
Research from 2025 reveals an “AI Productivity Paradox”: while AI coding assistants like GitHub Copilot increase code generation speed by approximately 55 percent, they can initially slow experienced developers by 19 percent due to increased code review and testing burdens. This paradox highlights why measuring AI impact requires looking beyond surface-level metrics.
Leading COOs are shifting from output metrics like “lines of code” to outcome metrics like merge rate and cycle time—the duration from commit to production. AI optimizes the entire Software Development Life Cycle by automating QA testing, generating documentation, and predicting merge conflicts before they occur.
Key insight: Measure AI-assisted throughput and cycle time rather than individual velocity. The goal isn’t faster coding but faster value delivery.
2. Revenue and Customer Operations: From Administration to Strategic Weapon
AI is transforming RevOps from reactive pipeline reviews to predictive revenue modeling. Instead of relying on customer success managers’ intuition, AI models analyze usage telemetry, support ticket sentiment, and login frequency to generate real-time customer health scores.
The impact is substantial. Companies using predictive scoring identify churn risk three to six months in advance, enabling proactive intervention rather than last-minute rescue attempts. For lower-tier SMB customers, AI agents autonomously handle contract renewals and negotiations, ensuring long-tail revenue isn’t lost due to limited human bandwidth.
This shift from manual pipeline reviews to automated lead scoring and from reactive support tickets to pre-emptive churn prevention represents a fundamental reimagining of customer operations.
3. Supply Chain Optimization and Resilience
AI’s ability to process large amounts of data in real time enables organizations to anticipate market trends, optimize logistics, and perform dynamic routing and scheduling. This capability becomes critical as supply chain complexity increases.
Contemporary operations leaders must balance traditional priorities – cost, quality, and service with two emerging pillars: resilience and sustainability. Understanding risk exposures across the supply chain requires mapping not just immediate suppliers but also second and third-tier dependencies that AI excels at analyzing.
Organizations are adopting more regional supply chain strategies, manufacturing and distributing products closer to end markets. This isn’t about abandoning globalization but about building structural resilience through geographic diversification and reduced exposure to international disruptions.
4. Knowledge Operations: Building the Corporate Brain
Technology companies face a critical challenge: information silos kill innovation. AI is solving what practitioners call “the discovery problem.”
Semantic search and Retrieval-Augmented Generation (RAG) systems allow employees to ask questions like “What was the decision logic for the pricing change in Q3?” and receive answers synthesized from Slack threads, JIRA tickets, and Google Docs. A global tech firm reduced internal search time by 60% by deploying an LLM-infused agent, effectively “de-siloing” institutional knowledge.
The shift from static wiki pages and intranets to active “corporate brains” represents one of AI’s most underappreciated operational impacts. When every employee can instantly access institutional memory, decision quality improves across the organization.
5. FinOps: Real-Time Cost Optimization
For SaaS companies, cloud hosting costs often rank as the second-largest expense after payroll. Traditional reactive cost management reviewing bills at month-end cannot address this scale of expenditure.
AI-native FinOps monitors cloud spend in real time, detecting anomalies like rogue training jobs left running. Automated right-sizing of instances and predictive capacity planning can reduce cloud spend by 30 to 40 percent compared to manual provisioning. This transforms finance operations from cost accounting to value optimization.
How COOs Implement AI in Operations Management
To avoid pilot purgatory, operations leaders should follow a phased implementation approach:
Phase 1: The Co-Pilot Layer (Individual Efficiency)
Deploy AI assistants that augment individual productivity, coding assistants for developers, writing assistants for marketing and sales teams. The goal is increasing personal throughput while managing the risk of “shadow AI” creating security vulnerabilities.
Phase 2: The Process Layer (Workflow Automation)
Implement AI in RevOps for forecasting and Customer Success Operations for ticket triage. Focus on reducing friction in handoffs between teams. For example, AI can summarize a prospect’s entire interaction history before a sales engineer joins a call, eliminating information loss at transition points.
Time tracking systems like actiTIME can provide the foundational data for understanding where handoff delays occur and measuring improvement after AI implementation.
Phase 3: The Agentic Layer (Autonomous Operations)
Deploy autonomous agents for internal IT support, Level 1 customer support, and basic FinOps right-sizing. The goal is achieving zero-touch operations for repetitive, low-stakes tasks while maintaining human oversight for high-stakes decisions.
Phase 4: Continuous Evolution (Ongoing)
Maintain curiosity about emerging AI capabilities and regularly reassess processes as technology advances. Foster a culture where questioning established methods is encouraged and learning is shared across the organization.
Navigating Critical Implementation Challenges
Data Governance as Strategy
You cannot build an AI-enabled operations layer on messy data. COOs must enforce data hygiene with the same rigor CFOs apply to GAAP compliance. This includes establishing clear data ownership, implementing validation protocols, and creating feedback loops that continuously improve data quality.
The Human-in-the-Loop Necessity
In high-stakes operations like deploying code to production, finalizing major contracts, or making significant resource allocation decisions AI should recommend, not decide. Over-reliance on AI “auto-merge” or “auto-reply” can lead to catastrophic outages or public relations crises.
Customer service provides a compelling example. AI agents often achieve higher satisfaction ratings than human interactions for routine inquiries, demonstrating that people embrace AI-powered service when it delivers value. However, complex, emotional, or high-value interactions still require human judgment and empathy.
Talent Reskilling and Cultural Transformation
As AI handles boilerplate code and basic support queries, junior roles evolve fundamentally. COOs must invest in upskilling staff to become “AI Editors” and “System Architects” earlier in their careers.
This challenge connects to a deeper cultural tension. Many organizations historically rewarded the “hero mentality” celebrating individuals who went above and beyond to fix crises. AI-enabled operations require the opposite: standardization, consistency, and following established processes. Helping teams understand that consistent execution adds as much value as heroic problem-solving requires intentional change management.
Workforce Implications: Augmentation, Not Elimination
Concerns about AI eliminating jobs often overshadow a more nuanced reality. The United States currently has approximately half a million open manufacturing jobs, with potential shortages reaching three million skilled workers by 2030 across manufacturing and construction.
AI provides opportunities for frontline workers to develop more valuable, transferable skills. Research suggests that 40 to 50 percent of a frontline worker’s job could shift over the next three to five years due to technology. Rather than viewing this as threatening, forward-thinking COOs see it as creating opportunities for workers to learn how to interact with advanced technology skills highly transferable beyond current roles.
Companies implementing real automation report better employee retention. When technology removes the tedious aspects of work that employees least enjoy, job satisfaction increases. Combined with skill development opportunities, this creates a virtuous cycle where technology and human capability reinforce each other.
Building Operational Resilience for an Uncertain World
The operational landscape is becoming structurally more complex rather than experiencing temporary volatility. COOs must build organizational muscles for continuous adaptation, capabilities that strengthen with exercise rather than solutions implemented once and forgotten.
Understanding exposure: Map risks across the entire supply chain, including people vulnerabilities, product dependencies, and geographic concentrations. AI excels at analyzing complex interdependencies and identifying potential failure points that escape human attention.
Multiple sources of supply: Single points of failure represent unacceptable risk in contemporary operations. Leading organizations maintain strategic redundancy in critical supply chains while using AI to optimize the cost-resilience tradeoff.
Dynamic adaptation: Recent events, from pandemics to geopolitical shifts, demonstrate that the world changes faster and more dynamically than ever. Organizations need systems that rapidly reconfigure as circumstances change, a capability AI enables when supported by accurate operational data and clear decision protocols.
The Continuous Improvement Foundation
Companies renowned for operational excellence, Toyota and its lean manufacturing philosophy being the canonical example, built their reputations on continuous improvement. The principle remains constant: make processes incrementally more efficient and effective every day.
In the AI era, continuous improvement extends beyond process refinement to encompass how technology enables transformation as external contexts shift. The mindset of curiosity, willingness to challenge established practices, and avoiding defensiveness about “how we’ve always done things” becomes even more critical when AI can rapidly test and implement new approaches.
Setting this tone from the top represents one of the COO’s most powerful levers. When leadership consistently demonstrates curiosity and openness to improvement, these values permeate through the organization, unlocking not only productivity gains but ideas that drive growth, service quality, and innovation.
Practical Metrics for Measuring AI Impact
Traditional operational metrics often fail to capture AI’s true impact. Consider these updated KPIs:
Engineering Operations:
- Cycle time from commit to production
- Merge rate and conflict resolution time
- Automated test coverage and pass rates
- Time spent on value-added versus maintenance activities
Revenue Operations:
- Predictive accuracy of revenue forecasting models
- Lead-to-customer conversion time
- Long-tail account retention rates
- Time from opportunity identification to deal closure
Customer Operations:
- Pre-emptive intervention success rate
- Time to resolution for complex versus routine issues
- Customer health score accuracy (predicted versus actual churn)
- AI resolution rate for Level 1 support
Knowledge Operations:
- Time to find critical information
- Cross-functional knowledge sharing frequency
- Reuse rate of existing solutions and documentation
- Employee-reported ease of information access
Financial Operations:
- Cloud cost per unit of value delivered
- Anomaly detection and response time
- Accuracy of capacity planning models
- Resource utilization efficiency
Looking Ahead: The Convergence of AI and Operations Research
Nearly four decades after Herbert Simon envisioned partnership between AI and operations research, his core argument proves remarkably prescient: the future lies in synthesizing structured model-driven optimization with flexible, data-driven AI methods. The synergy between these approaches is poised to drive significant advancements across multiple domains.
Research examining publications between 2010 and July 2024 reveals that AI integration in operations management spans five pivotal areas: supply chain management, revenue management, service operations, healthcare operations, and human-AI interaction. This breadth suggests we’re still in early stages of discovering AI’s full operational potential.
For COOs, this means the most exciting developments lie ahead. Operations leaders who have built strong foundations including robust data collection, cultures of continuous improvement, and collaborative cross-functional relationships will be best positioned to capitalize on emerging opportunities.
Conclusion: Embracing Transformation While Maintaining Humanity
There has never been a more challenging time to be in operations management, and simultaneously, never a more exciting time. AI is redefining what’s possible in productivity, resilience, and innovation, but success requires more than technology adoption.
The most effective operations leaders balance AI’s capabilities with human judgment, combining predictive analytics with contextual understanding, and automation with empathy. They recognize that operational excellence isn’t about choosing between technology and people but about orchestrating both toward shared goals.
As one industry leader recently observed: “The decision-making between the CIO and COO is getting fuzzier and fuzzier, and I think there’s a lot of strength to that.” This blurring of boundaries reflects a fundamental truth: in the AI era, operational excellence requires technological fluency, strategic thinking, and the ability to inspire organizations through continuous transformation.
The question isn’t whether AI will transform operations management, it already is. The question is whether your organization will lead that transformation or struggle to keep pace. With the right mindset, governance, and approach, COOs can position their organizations at the forefront of this operational revolution, creating value that seemed impossible just years ago.
